Who comes when the world goes Code Blue? A novel method of exploring job advertisements for COVID‐19 in health care

Abstract Aim To explore the health workforce responses to COVID‐19. Design Analysis of job advertisements. Methods We collected advertisements for healthcare jobs which were caused by and in response to COVID‐19 between 4 March–17 April 2020 for the United States, Canada, United Kingdom, Australia and New Zealand. We collected information on the date of the advertisement, position advertised and location. We categorized job positions into three categories: frontline, coordination and decision support. Results We found 952 job advertisements, 72% of which were from the United States. There was a lag period between reported COVID‐19‐confirmed cases and job advertisements by several weeks. Nurses were the most advertised position in every country. Frontline workers were substantially more demanded than coordination or decision‐support roles. Job advertisements are a novel data source which leverages a readily available information about how workforces respond to a pandemic. The initial phases of the response emphasise the importance of frontline workers, especially nurses.


| Aims
The aim of the study was to explore job advertisements for healthcare positions caused by and in response to COVID-19, in terms of the number and location of advertised jobs, numbers of advertised jobs relative to confirmed COVID-19 cases and positions advertised.

| Design
We conducted an analysis of COVID-19 job advertisements, defining a "COVID-19" job advertisement as an advertisement where the advertised position was caused by and in response to, SARS-CoV-2.
This means there was evidence that the job advertisement had arisen due to COVID-19 and the position was aiding the response to it. In addition to this, we only considered health jobs and jobs which were specifically supporting healthcare responses such as cleaners or administrators.

| Sample/Participants
We collected job advertisements through the online job board Indeed between 4 March-17 April 2020. We selected Indeed as it has country specific websites for each of the Five Eyes countries and Indeed is commonly used in those countries. For example, New Zealand currently has 14,000 job advertisements on Indeed (2.8 per 1,000 persons) and the United States has 2.6 m job advertisements (8 per 1,000 persons).

| Data collection
We searched for COVID-19 job advertisements by using the following search terms: "COVID," "corona" and "pandemic." Once we had collated these jobs, we filtered out false-positive results through a combination of keyword searches and manual identification (truepositive results were more likely to contain keywords such as emergency, response, COVID, urgent, etc.). We did not consider job advertisements which were exclusively in a language other than English (there were three Canadian advertisements exclusively in French which were excluded).
For job advertisements which fit our definition of a COVID-19 job, we collected the following data: title of position (e.g. "COVID-19 response urgent registered nurse"), job description, country and location advertised, the date the position was advertised and the date the information was collected. Locations of job advertisements were converted to geographic information through Google's GeoCoding API. In summary, the text description of a location (e.g. New York, NY) found in the job advertisement data was sent to Google, which processed the information and provided comprehensive, standardised information, including latitude, longitude and names of administrative regions (e.g. states, provinces).
The advertised positions (e.g. Registered Nurse, physical therapist) were categorised through iterative discussion with the research team. We categorised the advertised positions into roles (the above two examples would be categorised as "nursing" and "allied health," respectively) initially and categorised those roles into broad positions within the COVID-19 response: frontline, coordination and decision support (the above two examples would both be examples of "frontline" positions). The decision rules for categorisation are available in the Appendix S1.

| Data analysis
Analysis was undertaken using Python version 3.7.4 in a Jupyter Lab environment. Handling of datasets was undertaken using Pandas and figures were generated using Plotly.

| Validity, reliability and rigour
We compared our findings at a country level against confirmed cases of COVID-19. For this, we used data published by Our World in Data (Roser et al., 2020), which publishes cases collected by the European

Center for Disease Prevention and Control (European Centre for
Disease Prevention & Control, 2020). For comparisons at the state level in the United States, we used data from the New York Times GitHub repository (New York Times, 2020). This repository compiles time series data from state and local governments and health departments. For both data sources, we used confirmed cases, defined as individuals whose coronavirus infections were confirmed by laboratory test and reported by a federal, state or local government agency.
We chose to explore the state level response in the United States due to the large number of confirmed cases and job advertisements.   (between 20%-56% of all jobs) and frontline jobs were the most common advertised job category (between 78%-100% of jobs).

| RE SULTS/FINDING S
All advertised nursing jobs were frontline jobs. There were much smaller proportions of positions advertised for coordination or decision-support roles (0%-22% of jobs per country).

| D ISCUSS I ON
We explored COVID-19 healthcare jobs in the United States,  Therefore, our findings represent a subset of the workforce demand for COVID-19 jobs.
The dynamics of job advertisements warrant discussion, particularly the lag between cases and advertisements and the consistent increase in jobs over time. Firstly, the lag between the rise in cases of COVID-19 and the rise in job advertisements has multiple interpretations. Two likely interpretations are that existing personnel could handle the initial cases and that there was reluctance to advertise positions lest they were not needed. Another interpretation is that funding and approval for positions takes time, creating a delay between a surge in cases and a corresponding increase in advertisements. Secondly, a consistent increase in jobs is perhaps surprising given an exponential increase in confirmed cases, as a linear addition of frontline workers would soon be overwhelmed by cases. This may indicate that some hospital systems were yet to be overwhelmed, were internally reallocating workers or were constrained by budgets.
We found that US states differed in their rate of advertising We provided a sense of scale of the advertised demand for different workforces during the initial response of a pandemic. There is a large demand for frontline workers, a substantially smaller demand for coordination positions and a smaller demand still for decision support. This may be a methodological artefact, for instance if coordination or decision-support positions are more commonly internally reallocated than frontline workers. However, this finding is also unsurprising. Both coordination and decision-support activities enjoy economies of scale well above the activities of frontline healthcare workers, especially during a pandemic with exponential spread. It will be useful to observe how demand for these workforces changes over time. For instance, one might expect a rise in the demand for researchers over time as more data becomes available.
Our findings also highlight the importance and vulnerability of nurses during a pandemic. In the countries we examined, nurses were the most advertised position and this is not surprising, considering that key components of a public health response (screening, supportive therapy, health education) rely on nursing duties and expertise (Nayna Schwerdtle et al., 2020). However, this implies that more nurses will bear greater risks of the pandemic and the anxieties which come with such risks if cases are not controlled (Shanafelt et al., 2020). The nature of COVID-19 means that cases could overwhelm the supply of nurses and contingency plans should be considered. Initial suggestions have included expanding the pool of supply, such as students and retired nurses (Fraher et al., 2020) and might include the option to temporarily reallocate nurses across countries when overburdened. Our research has demonstrated how insight can be generated using job advertisements as a data source. Job advertisements are a readily accessible data source which can provide information on the speed, magnitude and composition of a pandemic response. Further research would be welcomed, particularly research about methods to efficiently extract and compare information from job descriptions. For instance, we note that the content from our collected job descriptions was fifty pages long, much of which is irrelevant to understanding the role, tasks and duties of the position. Comparing job descriptions may not currently be an effective use of time, but new methods to extract key information could make this less time intensive and more valuable. It is also important to acknowledge that job advertisements may not be filled. This means that inferences about the roles, tasks and duties assume that the position is filled and that the filled role does not deviate substantially from the description. Related to this, research which periodically looks for re-advertisements could be important, because researchers can ascertain a subset of positions which have not been filled which are highly demanded.

| Limitations
We will not have captured all relevant job advertisements that fit our definition. Rather, we have captured a subset of the "true-positive" job advertisements, where other true-positive job advertisements may have been found using other job boards. However, we chose to use Indeed due to its ubiquity in the countries of interest.
This, however, does not imply that similar groups in different countries use Indeed for the same purposes. For future investigations which focus on one country, an ensemble of job boards should be used, minimising the omission of key results. Furthermore, we were unable to identify "false negatives," that is jobs which fit our definition but were not captured by our search criteria. This is because jobs may have been a response to COVID-19 but may not mention this clearly in the job advertisement. It is also important to mention that there is no way to capture roles which have been created internally in an organisation, but which were not advertised publicly.

| CON CLUS ION
To the best of our knowledge, this is the first instance of job advertisements being used to monitor the response of workforces to a pandemic. Advertisements are an undervalued resource which can allow planners to look between countries and workforces, all in a relatively short amount of time.
In the context of COVID-19, we have shown that job advertisements can be useful in exploring how workforces respond to the initial stages of a public health crisis, in terms of who is required, in what proportion and where. In the initial phases of the pandemic, there is an increased demand for frontline healthcare workers and of all workers; nurses are the most in demand. Considering that nursing care provides limited economies of scale when faced with an exponentially increasing disease, consideration must be given to the welfare of nurses and to exploring options if demand exceeds supply.
Future research should investigate how the dynamics of workforce demand change over the course of the pandemic, and the potential use of other information contained in job advertisements, such as descriptions of tasks and duties. Institutions Australasia support through a CAPHIA scholarship.

CO N FLI C T O F I NTE R E S T
The authors declare they have no conflict of interest.

E TH I C A L A PPROVA L
As our data were freely available in the public domain and no identifying features were collected from the data, we did not require ethics approval.

PATI E NT CO N S E NT
This paper does not involve patients; therefore, no patient consent is needed.

DATA AVA I L A B I L I T Y S TAT E M E N T
The data used to inform this study are unavailable for public access at this time. Contact the corresponding author for further information.